Lack of spontaneous communication and delayed language acquisition have been recognized as among the most consistent challenges facing children diagnosed with autism spectrum disorder (Duffy & Healy, 2011; Kim, Junker, & Lord, 2014). Although research shows that the number of children with autism who speak fluently has increased beyond earlier approximations (Chiang & Carter, 2008), these challenges remain a central feature of the disorder as children with autism tend to initiate communication in fewer contexts compared to typical children (Duffy & Healy, 2011; Kossyvaki, Jones, & Guildberg, 2012). Although many researchers have studied the effectiveness of interventions aimed at eliciting spontaneous communication, only a few (e.g., Ploog, Scharf, Nelson, & Brooks, 2013) have focused attention on computer-based interventions.
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Review of Literature
Available scholarship demonstrates that most individuals with autism share common deficits in social and language skills, which include “failure to develop peer relationships, lack of engagement in play with peers, lack of emotion recognition, difficulties in communicative interactions, and generally poor social skills” (Ploog et al., 2013, p. 301). The inability of some individuals with autism to elicit spontaneous communication has interested many researchers (Bauminger-Zviely, Kimhi, & Agam-Ben-Artzi, 2014; Duffy & Healy, 2011).
Although the concept of spontaneous communication lacks a universal definition in the literature, it has generally been defined as “communicative behaviors that occur in the absence of prompts, instructions or other verbal cues” (Duffy & Healy, 2011, p. 977). Using this conceptualization, persons with a diagnosis of autism are said to lack spontaneity in their interactions as they often depend on prompts such as oral cues, modeling, and material direction to induce some form of communication.
Several scholars have focused attention on the concept of spontaneous communication and its relationship to autism. In their study, Chiang and Carter (2008) define “spontaneous verbalizations as communicative behaviors in response to nonverbal stimuli, in the absence of verbal discriminative stimuli” (p. 698). These authors are clear that spontaneous communication must occur in the absence of questions and without partner prompting, meaning that it normally takes place without specified antecedents.
However, another study by Loncola and Graig-Unkefer (2005) shows that spontaneous communication is said to occur when a child utters something that may be directed to another child or object but not prompted by an adult. Such an utterance, according to these authors, usually occurs within 10 seconds of a specified antecedent. This view is reinforced by Potter and Whittaker (2001), who argue that spontaneous behaviors should not be judged by the existence of specific antecedents but rather by their demonstration to meet the communication requirements of the situation and their functionality in the given context. The description given by Potter and Whittaker (2001) provides some proof that computer-generated visual images may indeed be used as an intervention to elicit spontaneous communication in children with autism.
Some researchers have studied how the use of computer-assisted technology (CAT) influences the social, communicative, and language development in individuals who have been diagnosed with autism. One review by Duffy and Healy (2011) revealed that CAT and Applied Behavior Analysis can be incorporated together to enhance spontaneous communication in individuals with a diagnosis of autism through the use of procedures such as “time delay/prompt fading, milieu language teaching, direct instruction, script fading, and fluency training” (p. 978).
Another review by Stromer, Kimball, Kinney, and Taylor (2006) found that computer-generated activity schedules can be used to encourage learning about multiple cues and reinforce functional verbal skills of children with autism by increasing attention to projected stimuli. The capacity of computers to simultaneously integrate vocal and video cues was found to be particularly beneficial as it can be used to pair static visual support with additional instructional stimuli such as audio and video recordings to trigger social, communicative, and language development in children with autism (Stromer et al., 2006).
Another review by Ploog, Scharf, Nelson, and Brooks (2013) found that most researchers have recognized the potential of computer technology as an effective and efficient tool in research and treatment of autism as most children show a high affinity to computers and professionals using CAT are more likely to implement treatments with higher precision and less variability than those using traditional treatment approaches.
The review found that CAT may provide new ways of teaching language skills to children with autism, particularly in terms of increasing the use of voluntary speech for social communication, reinforcing the skills needed for enhanced adaptive functioning, and providing a higher precision in training (Ploog et al., 2013). Some of the flaws associated with the use of CAT noted in the review include reinforcement of undesirable behavior, a distraction from the relevant information intended to be conveyed, increased social isolation due to interaction with computers rather than humans, and encouragement of limited acts of behavior and ways of responding to various stimuli.
Need for the Proposed Study/Problem Statement
Despite recent advances in technology and the overflowing scholarship on autism and associated interventions, a fundamental question that remains unanswered is whether computer-based approaches are effective in eliciting spontaneous communication in children with a diagnosis of autism. Unfortunately, as reported by several scholars (e.g., Goldsmith & LeBlanc, 2004; Ploog et al., 2013), research studies that can be used to answer this question are sparse and highly fragmented.
Additionally, the few studies that focus on this area are exploratory and fail to demonstrate a rigorous assessment of the effectiveness of computer-based approaches (Duffy & Healy, 2011; Ploog et al., 2013). As such, many language therapists may still be unsure about the efficacy of computer-based interventions judging by assertions that more rigorous assessments need to be done to convincingly demonstrate the efficacy of these interventions. The proposed study aims to fill these gaps by evaluating the efficacy of computer-generated stimuli in eliciting spontaneous communication.
Drawing from the problem statement, the proposed study seeks to use professional language therapists to evaluate the effectiveness of computer-generated stimuli in reinforcing spontaneous communication in children with autism. The independent variable for the proposed study is computer-generated stimuli, while the dependent variable is spontaneous communication.
The independent/dependent variable relation that will be addressed in the proposed study is how exposure to computer-generated stimuli can generate spontaneous communicative episodes (e.g., vocalizations elicited in trying to ask for something, clarify a visual image, describe a visual image, or retell a video story) among children diagnosed with autism. These variables (computer-generated stimuli and spontaneous communication) are empirical since “they deal with objects in the observable physical world surrounding us” (Iversen & Gergen, 1997, p. 14). Their delimitation is described below.
Computer-generated stimuli will be conceptually defined as a thing or event that is performed by a computer system to elicit a particular functional reaction or to provide an incentive to behave in a particular way (Duffy & Healy, 2011). However, the concept will be operationalized as audio or visual incentives that are generated by a computer system to evoke a specific communicative reaction among children with autism. In the proposed paper, computer-generated stimuli will be delimited to incentives provided by computer-generated video images (video modeling), virtual reality, and audio-visual (multimedia) technology.
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Spontaneous communication is conceptually defined as “the vocalization of concepts generated by the speaker” (Chiang & Carter, 2008, p. 694). In the proposed study, spontaneous communication will be operationalized as the capability of children with autism to demonstrate some form of communicative behaviors in the absence of physical prompts, instructions, or other orally-initiated cues. The spontaneous communicative behaviors will be delimited to the therapists’ feedback on their observation of children who are exposed to computer-generated stimuli to develop their communication skills. The issue of interest will be whether the children attempt to clarify something, tell a picture story, describe a picture, or retell a story without oral prompts (Kim, Junker, & Lord, 2014).
Thirty (30) professional language therapists who use computer-based interventions to treat and manage children with autism will be enrolled in the study through purposive sampling. The therapists will be required to provide their perceptions about the effectiveness of computer-generated stimuli in encouraging spontaneous communication in children diagnosed with autism.
Internal Validity and Factors that Compromise Internal Validity
To achieve the required internal validity in the proposed study, the “variations in the dependent variable [must] originate from variations in the independent variable(s) and not from other confounding factors” (Balnaves & Caputi, 2001, p. 234). The variables that can compromise internal validity in the proposed study include (1) inadequate understanding of stimuli, (2) participants’ understanding of selected interventions, (3) researcher perceptions, (4) unrepresentative sample, (5) participant dropout, and (6) ineffective grading criteria of the perceptions provided by participants.
These variables can be categorized into factors which include instrumentation errors, subject/participant effects, mortality, and selection bias (Creswell, 2013). Researcher effects may present in the form of deliberate or unintentional attributes or expectations of the researcher that may influence the study participants, while instrumentation errors may appear in the form of ineffective measurement scales in the questionnaire as well as lack of understanding of various stimuli and how they affect spontaneous communication.
Additionally, subject/participant effects in the proposed study may take the form of inadequate understanding of stimuli as well as insufficient participant understanding of selected interventions and their effectiveness. The mortality problem may occur when some subjects fail to participate in the research study, resulting in a bias. Lastly, selection bias may occur due to failure by the researcher to use an effective sampling strategy to get the language therapists who will take part in the study (Balnaves & Caputi, 2001).
Plan to Control the Factors
The researcher intends to remain objective and also to pilot the data collection instrument with the view to addressing researcher effects and instrumentation errors. Participants will also be taken through the data collection instrument to ensure that they understand all the items and hence reduce response errors that may be related to instrumentation (Balnaves & Caputi, 2001).
To deal with subject/participant effects, the questionnaire will be administered only once and efforts will be made to ensure that participants are comfortable when responding to the questions contained in the questionnaire. The mortality factor will be addressed by educating the selected subjects on the need to participate in the study and also by removing bottlenecks to participation (e.g., distance, time, and location). Lastly, the researcher intends to address selection bias by using purposive sampling technique to ensure that all participants will have deep and insightful knowledge about the interventions under investigation and their effectiveness (Creswell, 2013).
Quantitative data used to evaluate the effectiveness of the two interventions will be collected from sampled speech therapists through the use of a standardized questionnaire containing 5-point Lickert-type questions. Most of the items in the questionnaire will be closed-ended with possible responses as “strongly agree”, “agree”, “not sure/no opinion”, “disagree”, and “strongly disagree” (Balnaves & Caputi, 2001, p. 267).
The questionnaire will be administered online to remove the time and distance constraints that often lead to problems associated with mortality. A pilot study involving three language therapists will be administered before the actual data collection phase to ensure that the questionnaire is reliable and valid in terms of consistency of measurement and ability to measure what it is intended to measure.
Data collected from the field will be analyzed using a statistical program known as SPSS (Statistical Package for Social Sciences) with the view to providing answers to the noted research gaps. Descriptive statistics will be used to investigate the effectiveness of the computer-based intervention based on percentages, mean scores, and standard deviations of various questions contained in the questionnaire.
According to Balnaves and Caputi (2001), descriptive statistics are highly preferred in this type of study as they not only use measures of central tendency (e.g., mean scores) to describe the most typical value in a data set (in this case, effectiveness) but also employ measures of dispersion (e.g., variance and standard deviation) to describe the variability of the responses given. It is also possible to present findings using frequency distributions and graphical presentations that are created by the software program.
Balnaves, M., & Caputi, P. (2001). Introduction to quantitative research methods: An investigative approach. Thousand Oaks, CA: Sage Publications Inc.
Bauminger-Zviely, K., Kimhi, Y., & Agam-Ben-Artzi, G.A. (2014). Spontaneous peer conversation in preschoolers with high-functioning autism spectrum disorder versus typical development. Journal of Child Psychology and Psychiatry, 55(4), 363-373.
Chiang, H.M., & Carter, M. (2008). Spontaneity of communication in individuals with autism. Journal of Autism & Developmental Disorders, 38(4), 693-705.
Creswell, J.W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage Publications Inc.
Duffy, C., & Healy, O. (2011). Spontaneous communication in autism spectrum disorder: A review of topographies and interventions. Research in Autism Spectrum Disorders, 5(2), 977-983.
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Kim, S.H., Junker, D., & Lord, C. (2014). Observation of spontaneous expressive language (OSEL): A new measure for spontaneous and expressive language of children with autism spectrum disorders and other communication disorders. Journal of Autism and Developmental Disorders, 44(12), 3230-3244.
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Ploog, B., Scharf, A., Nelson, D., & Brooks, P. (2013). Use of computer-assisted technologies (CAT) to enhance social, communicative, and language development in children with autism spectrum disorders. Journal of Autism & Developmental Disorders, 43(2), 301-322.
Potter, C., & Whittaker, C. (2001). Enabling communication in children with autism. London: Jessica Kingsley.
Stromer, R., Kimball, J.W., Kinney, E.M., & Taylor, B. (2006). Activity schedules, computer technology, and teaching children with autism spectrum disorders. A Focus on Autism & Other Developmental Disabilities, 21(1), 14-24.